With the rapid growth of data scale and composition complexity in the real-world applications, it is an important challenge for current clustering algorithms to estimate the number and the centers of clusters accurately in processing and analyzing the complex and large-scale data. The accurate estimation of cluster number and cluster centers is crucial for partial parametric clustering algorithm, complexity measurement and simplified representation of dataset. In this paper, grounded on the in-depth analysis of I-nice, a multi-observation I-nice clustering algorithm based on candidate centers fusion(I-niceCF) is proposed. Based on the original multi-observation projection divide-and-conquer framework, Gaussian mixture model(GMM) is combined with the coarse-to-fine optimal mixture model search strategy to partition data subsets exactly. In addition, GMM component vectors of candidate centers are constructed based on the distance of candidate centers from each observation point and optimal GMMs. A Minkowski distance pair is designed to measure the dissimilarity between candidate centers. Finally, the candidate centers are fused based on the mixture component vectors. Different from the existing clustering algorithms, I-niceCF is jointly optimized by data subset partitioning of divide-and-conquer process and candidate centers fusion. Consequently, accurate and efficient estimation for hundreds of clusters is achieved. A series of experiments on real and synthetic datasets show that I-niceCF can estimate cluster number and cluster centers more accurately with higher clustering accuracy and its stability under various data scenarios is verified.